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Section A. Twelve methods of calculating forecasts are available. Most of these methods provide for limited user control.
In virtually every decision they make, executives today consider some kind of forecast. Sound predictions of demands and trends are no longer luxury items, but a necessity, if managers are to cope with seasonality, sudden changes in demand levels, price-cutting maneuvers of the competition, strikes, and large swings of the economy.
Forecasting can help them […]. Forecasting can help them deal with these troubles; but it can help them more, the more they know about the general principles of forecasting, what it can and cannot do for them currently, and which techniques are suited to their needs of the moment. Here the authors try to explain the potential of forecasting to managers, focusing special attention on sales forecasting for products of Corning Glass Works as these have matured through the product life cycle.
Also included is a rundown of forecasting techniques. To handle the increasing variety and complexity of managerial forecasting problems, many forecasting techniques have been developed in recent years. Each has its special use, and care must be taken to select the correct technique for a particular application.
These factors must be weighed constantly, and on a variety of levels. In general, for example, the forecaster should choose a technique that makes the best use of available data. This kind of trade-off is relatively easy to make, but others, as we shall see, require considerably more thought.
The availability of data and the possibility of establishing relationships between the factors depend directly on the maturity of a product, and hence the life-cycle stage is a prime determinant of the forecasting method to be used. Our purpose here is to present an overview of this field by discussing the way a company ought to approach a forecasting problem, describing the methods available, and explaining how to match method to problem. We shall illustrate the use of the various techniques from our experience with them at Corning, and then close with our own forecast for the future of forecasting.
Although we believe forecasting is still an art, we think that some of the principles which we have learned through experience may be helpful to others.
A manager generally assumes that when asking a forecaster to prepare a specific projection, the request itself provides sufficient information for the forecaster to go to work and do the job.
This is almost never true. Successful forecasting begins with a collaboration between the manager and the forecaster, in which they work out answers to the following questions. What is the purpose of the forecast—how is it to be used?
This determines the accuracy and power required of the techniques, and hence governs selection. Deciding whether to enter a business may require only a rather gross estimate of the size of the market, whereas a forecast made for budgeting purposes should be quite accurate. The appropriate techniques differ accordingly. Forecasts that simply sketch what the future will be like if a company makes no significant changes in tactics and strategy are usually not good enough for planning purposes.
On the other hand, if management wants a forecast of the effect that a certain marketing strategy under debate will have on sales growth, then the technique must be sophisticated enough to take explicit account of the special actions and events the strategy entails.
Techniques vary in their costs, as well as in scope and accuracy. The manager must fix the level of inaccuracy he or she can tolerate—in other words, decide how his or her decision will vary, depending on the range of accuracy of the forecast. This allows the forecaster to trade off cost against the value of accuracy in choosing a technique. For example, in production and inventory control, increased accuracy is likely to lead to lower safety stocks.
Here the manager and forecaster must weigh the cost of a more sophisticated and more expensive technique against potential savings in inventory costs. Exhibit I shows how cost and accuracy increase with sophistication and charts this against the corresponding cost of forecasting errors, given some general assumptions. The most sophisticated technique that can be economically justified is one that falls in the region where the sum of the two costs is minimal.
Once the manager has defined the purpose of the forecast, the forecaster can advise the manager on how often it could usefully be produced. From a strategic point of view, they should discuss whether the decision to be made on the basis of the forecast can be changed later, if they find the forecast was inaccurate.
If it can be changed, they should then discuss the usefulness of installing a system to track the accuracy of the forecast and the kind of tracking system that is appropriate. What are the dynamics and components of the system for which the forecast will be made? This clarifies the relationships of interacting variables. Generally, the manager and the forecaster must review a flow chart that shows the relative positions of the different elements of the distribution system, sales system, production system, or whatever is being studied.
Note the points where inventories are required or maintained in this manufacturing and distribution system—these are the pipeline elements, which exert important effects throughout the flow system and hence are of critical interest to the forecaster. Where data are unavailable or costly to obtain, the range of forecasting choices is limited. The flow chart should also show which parts of the system are under the control of the company doing the forecasting.
In Exhibit II, this is merely the volume of glass panels and funnels supplied by Corning to the tube manufacturers. In the part of the system where the company has total control, management tends to be tuned in to the various cause-and-effect relationships, and hence can frequently use forecasting techniques that take causal factors explicitly into account. The flow chart has special value for the forecaster where causal prediction methods are called for because it enables him or her to conjecture about the possible variations in sales levels caused by inventories and the like, and to determine which factors must be considered by the technique to provide the executive with a forecast of acceptable accuracy.
Once these factors and their relationships have been clarified, the forecaster can build a causal model of the system which captures both the facts and the logic of the situation—which is, after all, the basis of sophisticated forecasting. How important is the past in estimating the future? Significant changes in the system—new products, new competitive strategies, and so forth—diminish the similarity of past and future.
Over the short term, recent changes are unlikely to cause overall patterns to alter, but over the long term their effects are likely to increase. The executive and the forecaster must discuss these fully. Once the manager and the forecaster have formulated their problem, the forecaster will be in a position to choose a method. There are three basic types— qualitative techniques, time series analysis and projection, and causal models.
The first uses qualitative data expert opinion, for example and information about special events of the kind already mentioned, and may or may not take the past into consideration.
The second, on the other hand, focuses entirely on patterns and pattern changes, and thus relies entirely on historical data. The third uses highly refined and specific information about relationships between system elements, and is powerful enough to take special events formally into account.
As with time series analysis and projection techniques, the past is important to causal models. These differences imply quite correctly that the same type of forecasting technique is not appropriate to forecast sales, say, at all stages of the life cycle of a product—for example, a technique that relies on historical data would not be useful in forecasting the future of a totally new product that has no history.
The major part of the balance of this article will be concerned with the problem of suiting the technique to the life-cycle stages. We hope to give the executive insight into the potential of forecasting by showing how this problem is to be approached.
But before we discuss the life cycle, we need to sketch the general functions of the three basic types of techniques in a bit more detail. Primarily, these are used when data are scarce—for example, when a product is first introduced into a market. They use human judgment and rating schemes to turn qualitative information into quantitative estimates.
The objective here is to bring together in a logical, unbiased, and systematic way all information and judgments which relate to the factors being estimated. Some of the techniques listed are not in reality a single method or model, but a whole family. Thus our statements may not accurately describe all the variations of a technique and should rather be interpreted as descriptive of the basic concept of each.
A disclaimer about estimates in the chart is also in order. Estimates of costs are approximate, as are computation times, accuracy ratings, and ratings for turning-point identification. The costs of some procedures depend on whether they are being used routinely or are set up for a single forecast; also, if weightings or seasonals have to be determined anew each time a forecast is made, costs increase significantly.
Still, the figures we present may serve as general guidelines. The reader may find frequent reference to this gate-fold helpful for the remainder of the article. Once they are known, various mathematical techniques can develop projections from them. The matter is not so simple as it sounds, however. It is usually difficult to make projections from raw data since the rates and trends are not immediately obvious; they are mixed up with seasonal variations, for example, and perhaps distorted by such factors as the effects of a large sales promotion campaign.
The raw data must be massaged before they are usable, and this is frequently done by time series analysis. Time series analysis helps to identify and explain:. Unfortunately, most existing methods identify only the seasonals, the combined effect of trends and cycles, and the irregular, or chance, component.
That is, they do not separate trends from cycles. We shall return to this point when we discuss time series analysis in the final stages of product maturity.
We should note that while we have separated analysis from projection here for purposes of explanation, most statistical forecasting techniques actually combine both functions in a single operation.
It is obvious from this description that all statistical techniques are based on the assumption that existing patterns will continue into the future. This assumption is more likely to be correct over the short term than it is over the long term, and for this reason these techniques provide us with reasonably accurate forecasts for the immediate future but do quite poorly further into the future unless the data patterns are extraordinarily stable.
For this same reason, these techniques ordinarily cannot predict when the rate of growth in a trend will change significantly—for example, when a period of slow growth in sales will suddenly change to a period of rapid decay. Such points are called turning points. They are naturally of the greatest consequence to the manager, and, as we shall see, the forecaster must use different tools from pure statistical techniques to predict when they will occur.
When historical data are available and enough analysis has been performed to spell out explicitly the relationships between the factor to be forecast and other factors such as related businesses, economic forces, and socioeconomic factors , the forecaster often constructs a causal model. A causal model is the most sophisticated kind of forecasting tool. It expresses mathematically the relevant causal relationships, and may include pipeline considerations i. It may also directly incorporate the results of a time series analysis.
The causal model takes into account everything known of the dynamics of the flow system and utilizes predictions of related events such as competitive actions, strikes, and promotions. If the data are available, the model generally includes factors for each location in the flow chart as illustrated in Exhibit II and connects these by equations to describe overall product flow.
If certain kinds of data are lacking, initially it may be necessary to make assumptions about some of the relationships and then track what is happening to determine if the assumptions are true. Typically, a causal model is continually revised as more knowledge about the system becomes available.
Again, see the gatefold for a rundown on the most common types of causal techniques. As the chart shows, causal models are by far the best for predicting turning points and preparing long-range forecasts. At each stage of the life of a product, from conception to steady-state sales, the decisions that management must make are characteristically quite different, and they require different kinds of information as a base. The forecasting techniques that provide these sets of information differ analogously.
Exhibit III summarizes the life stages of a product, the typical decisions made at each, and the main forecasting techniques suitable at each.
Equally, different products may require different kinds of forecasting. Two CGW products that have been handled quite differently are the major glass components for color TV tubes, of which Corning is a prime supplier, and Corning Ware cookware, a proprietary consumer product line. We shall trace the forecasting methods used at each of the four different stages of maturity of these products to give some firsthand insight into the choice and application of some of the major techniques available today.
Many of the changes in shipment rates and in overall profitability are therefore due to actions taken by manufacturers themselves.
It uses state-of-the-art modeling techniques to produce high quality forecasts with minimal human intervention. Forecasts produced by the RDF system enhance the retailer's supply-chain planning, allocation, and replenishment processes, enabling a profitable and customer-oriented approach to predicting and meeting product demand. Today's progressive retail organizations know that store-level demand drives the supply chain. The ability to forecast consumer demand productively and accurately is vital to a retailer's success. The business requirements for consumer responsiveness mandate a forecasting system that more accurately forecasts at the point of sale, handles difficult demand patterns, forecasts promotions and other causal events, processes large numbers of forecasts, and minimizes the cost of human and computer resources. Forecasting drives the business tasks of planning, replenishment, purchasing, and allocation. As forecasts become more accurate, businesses run more efficiently by buying the right inventory at the right time.
In virtually every decision they make, executives today consider some kind of forecast. Sound predictions of demands and trends are no longer luxury items, but a necessity, if managers are to cope with seasonality, sudden changes in demand levels, price-cutting maneuvers of the competition, strikes, and large swings of the economy. Forecasting can help them […]. Forecasting can help them deal with these troubles; but it can help them more, the more they know about the general principles of forecasting, what it can and cannot do for them currently, and which techniques are suited to their needs of the moment. Here the authors try to explain the potential of forecasting to managers, focusing special attention on sales forecasting for products of Corning Glass Works as these have matured through the product life cycle.
Firstly, because in any retail or supply chain planning context, forecasting is always a means to an end, not the end itself. We need to keep in mind that a forecast is relevant only in its capacity of enabling us to achieve other goals, such as improved on-shelf availability, reduced food waste, or more effective assortments. Secondly, although forecasting is an important part of any planning activity, it still represents only one cogwheel in the planning machinery, meaning that there are other factors that may have a significant impact on the outcome. Oftentimes the importance of accurate forecasting is truly crucial, but from time to time other factors are more important to attaining the desired results. We are, of course, not saying that you should stop measuring forecast accuracy altogether. It is an important tool for root cause analysis and for detecting systematic changes in forecast accuracy early on. The role of demand forecasting in attaining business results.
How much safety stock should we hold in case demand is higher than the forecast of units? But incorporating uncertainty into forecasts is not straightforward.
OR-Notes are a series of introductory notes on topics that fall under the broad heading of the field of operations research OR. They are now available for use by any students and teachers interested in OR subject to the following conditions. A full list of the topics available in OR-Notes can be found here. The forecast for month six is just the moving average for the month before that i. To compare the two forecasts we calculate the mean squared deviation MSD.
Use the power of data to drive next-level customer relationships. Are AI and machine learning the future for demand forecasting? AI and machine learning overcome forecasting limitations.
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